GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy

Abstract

Uncertainty associated with an optimal number of macroeconomic variables to be used in factor model is challenging since there is no criteria which states what kind of data should be used, how many variables to employ and does disaggregated data improve factor model’s forecasts.
The paper studies an impact of nested macroeconomic data on Latvian GDP forecasting accuracy within factor modelling framework. Nested data means disaggregated data or sub-components of aggregated variables. We employ Stock-Watson factor model in order to estimate factors and to make GDP projections two periods ahead. Root mean square error is employed as the standard tool to measure forecasting accuracy. According to this empirical study we conclude that additional information that contained in disaggregated components of macroeconomic variables could be used to enhance Latvian GDP forecasting accuracy. The efficiency gain improving forecasts is about 0.15-0.20 percentage points of year on year quarterly growth for the forecasting period 1 quarter ahead, but for 2 quarter ahead it’s about half percentage point.

Item Type:

MPRA Paper

Original Title:

GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy

English Title:

GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy

Caggiano, G., Kapetanios., G., and Labhard, V., (2009). Are more data always better for factor analysis? Results for the Euro Area, the six largest Euro Area countries and UK. ECB Working Paper, No. 1051.